Advancing the efficiency and efficacy of patient reported outcomes with multivariate computer adaptive testing.

OBJECTIVE: The Patient Reported Outcomes Measurement Information System (PROMIS) initiative developed an array of patient reported outcome (PRO) measures. To reduce the number of questions administered, PROMIS utilizes unidimensional item response theory and unidimensional computer adaptive testing (UCAT), which means a separate set of questions is administered for each measured trait. Multidimensional item response theory (MIRT) and multidimensional computer adaptive testing (MCAT) simultaneously assess correlated traits. The objective was to investigate the extent to which MCAT reduces patient burden relative to UCAT in the case of PROs. METHODS: One MIRT and 3 unidimensional item response theory models were developed using the related traits anxiety, depression, and anger. Using these models, MCAT and UCAT performance was compared with simulated individuals. RESULTS: Surprisingly, the root mean squared error for both methods increased with the number of items. These results were driven by large errors for individuals with low trait levels. A second analysis focused on individuals aligned with item content. For these individuals, both MCAT and UCAT accuracies improved with additional items. Furthermore, MCAT reduced the test length by 50%. DISCUSSION: For the PROMIS Emotional Distress banks, neither UCAT nor MCAT provided accurate estimates for individuals at low trait levels. Because the items in these banks were designed to detect clinical levels of distress, there is little information for individuals with low trait values. However, trait estimates for individuals targeted by the banks were accurate and MCAT asked substantially fewer questions. CONCLUSION: By reducing the number of items administered, MCAT can allow clinicians and researchers to assess a wider range of PROs with less patient burden.

An innovative symptom monitoring tool for people with advanced lung cancer: a pilot demonstration.

Treatment for advanced lung cancer is not curative; therefore, the primary goals of its care are to maximize symptom management and minimize treatment toxicity. Increasingly, patient-reported symptoms and health-related quality of life (HRQL) outcomes have been accepted as important endpoints; several validated measures have gained wide acceptance in research, but their use in practice has been limited. Computer technology increasingly is used to reduce patient and administrative burden in conducting assessments to produce a real-time presentation of symptom and HRQL data. This paper describes a technology-based monitoring system developed for patients with advanced lung cancer who were starting chemotherapy. Among the 90 participants, compliance with the weekly symptom survey was 92%. Patient acceptability of the system was high, as evidenced by 30 patients who elected to complete an additional monitoring interval beyond the 12-week study period. Of patients who reported discussing their responses with a provider (95%), a majority (69%) stated that the questionnaire helped them to focus on issues to be discussed with their physicians. The system also was favorably reviewed by physicians, who indicated that the report helped them to compare patients' responses over time. Next steps will include a randomized trial to test the system's efficacy in improving symptom management.